Improving the performance of Success Likelihood Index Model (SLIM) using Bayesian Network

Conference paper (2019)

Authors

S. Abrishami Ferdowsi University of Mashhad
N. Khakzad Rostami Safety and Security Science - TPM
P.H.A.J.M. van Gelder Safety and Security Science - TPM
Seyed Mahmoud Hosseini Ferdowsi University of Mashhad
Research Group
Safety and Security Science (TPM) (TU Delft)
Copyright: © 2019 S. Abrishami, N. Khakzad, P.H.A.J.M. van Gelder, Seyed Mahmoud Hosseini
DOI
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https://doi.org/10.3850/978-981-11-2724-3_0248-cd
Uncertainty Bayesian network Human error probability Success likelihood index model
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Published Date

2019

Language

English

Reuse Rights

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Faculty

Technology, Policy and Management

Department

Values Technology and Innovation

Research Group

Safety and Security Science

Abstract

Success Likelihood Index Model (SLIM) is one of the widely-used methods in human reliability assessment especially when data is insufficient. However, this method suffers from uncertainty as it heavily relies on expert judgment for determining the model parameters such as the rates and weights of the performance shaping factors. The present study is aimed at using Bayesian Network (BN) for improving the performance of SLIM in handling the uncertainty arising from experts opinion and lack of data. To this end, SLIM is combined with BN to form the so-called BN-SLIM technique. We applied both SLIM and BN-SLIM models to a hypothetical example and compared the results. It is shown that BN-SLIM is able to provide a better estimation of human error probability by considering dependencies. The probability updating feature of BN-SLIM in particular makes it possible to use new information to update the prior beliefs about the rates of the performance shaping factors, thus updating the resultant human error probabilities.

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